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USING KIBANA AND ELASTICSEARCH FOR THE
RECOMMENDATION OF JOB OFFERS TO STUDENTS
Antonio Robles-Gómez, Salvador Ros, Antoni...
OUTLINE
 Motivation
 Information Retrieval
 Indicators
 Recommendation System
 Conclusions
2
 Current necessity of comparing personal studies with professional
profiles in order to make recommendations about employ...
MOTIVATION (II)
Type Level Who Benefits?
Learning
Analytics
Educational data
mining
Course-level: social
networks, concept...
 Several sources of information oriented to the professional market:
 It is essential to extract a common vocabulary in ...
OUTLINE
 Motivation
 Information Retrieval
 Indicators
 Recommendation System
 Conclusions
6
 There is two ways of accessing data sources:
 Some of them provides with APIs to access the requested information
 Usi...
INFORMATION RETRIEVAL (II)
 The organization of information
is not clear and depends on the
particular professional netwo...
INFORMATION RETRIEVAL (III)
9
INFORMATION RETRIEVAL (IV)
 Elasticsearch for
storing the gathered
data
 Kibana for the
visualization of the
Information...
OUTLINE
 Motivation
 Information Retrieval
 Indicators
 Recommendation System
 Conclusions and Future Work
11
 Selected indicators:
 Title
 Description
 Duration
 Category and sub-category
 Company name and type
 Location: ci...
 Example:
 Java programmer
 This position focuses on programming physical devices
 2 years duration
 Computers and te...
14
INDICATORS (III)
Antonio Robles-Gómez, Salvador Ros, Antonio Martínez-Gámez, Roberto Hernández,
Agustín C. Caminero, Ll...
 As a first approximation:
 An educational profile includes the last and previous Degree Titles and Degree Title Types,
...
OUTLINE
 Motivation
 Information Retrieval
 Indicators
 Recommendation System
 Conclusions and Future Work
16
17
RECOMMENDATION SYSTEM (I)
 Developing a web application in order
to recommend job offers to University
students
 Stud...
18
RECOMMENDATION SYSTEM (II)
19
RECOMMENDATION SYSTEM (III)
OUTLINE
 Motivation
 Information Retrieval
 Indicators
 Recommendation System
 Conclusions
20
 Learning does not take place in an isolated context, it is linked with the
professional profiles
 Several professional ...
USING KIBANA AND ELASTICSEARCH FOR THE
RECOMMENDATION OF JOB OFFERS TO STUDENTS
Antonio Robles-Gómez, Salvador Ros, Antoni...
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VII Jornadas eMadrid "Education in exponential times". Antonio Robles, "Using Kibana and ElasticSearch for the Recommendation of Job Offers to Students". 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Antonio Robles, "Using Kibana and ElasticSearch for the Recommendation of Job Offers to Students". 04/07/2017.

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VII Jornadas eMadrid "Education in exponential times". Antonio Robles, "Using Kibana and ElasticSearch for the Recommendation of Job Offers to Students". 04/07/2017.

  1. 1. USING KIBANA AND ELASTICSEARCH FOR THE RECOMMENDATION OF JOB OFFERS TO STUDENTS Antonio Robles-Gómez, Salvador Ros, Antonio Martínez-Gámez, Roberto Hernández, Llanos Tobarra, Rafael Pastor, Agustín C. Caminero, Jesús Cano {arobles,sros,roberto,llanos,rpastor,accaminero,jcano}@scc.uned.es martinezgamez.antonio@gmail.com
  2. 2. OUTLINE  Motivation  Information Retrieval  Indicators  Recommendation System  Conclusions 2
  3. 3.  Current necessity of comparing personal studies with professional profiles in order to make recommendations about employability needs  Objective:  To improve users’ professional career  Example:  A user owns a particular degree, but most of his/her jobs of interest require a higher degree  It would be desirable to recommend him/her to start a particular post-degree 3 MOTIVATION (I)
  4. 4. MOTIVATION (II) Type Level Who Benefits? Learning Analytics Educational data mining Course-level: social networks, conceptual development, discourse analysis, intelligent curriculum… Learners, Faculty Departmental: predictive modeling, patterns of success/failure… Learners, Faculty Academic Analytics Institutional: learner profiles, performance of academics, knowledge flow… Learners, Faculty, Administrators Regional: comparisons between systems Administrators, Funders, Marketing National and International: comparisons between National governments, Education 4
  5. 5.  Several sources of information oriented to the professional market:  It is essential to extract a common vocabulary in order to make professional recommendations  Speak a similar language, independently of the source of information  Professional social networks:  InfoJobs, Monster, LinkedIn… 5 MOTIVATION (III)
  6. 6. OUTLINE  Motivation  Information Retrieval  Indicators  Recommendation System  Conclusions 6
  7. 7.  There is two ways of accessing data sources:  Some of them provides with APIs to access the requested information  Using crawling/scraping techniques to access the desirable information, due to the lack some of them are not directly accessible for public usage  For instance, if you request access to the LinkedIn API, they usually does not allow accessing its API, in spite of justifying your intentions 7 INFORMATION RETRIEVAL (I)
  8. 8. INFORMATION RETRIEVAL (II)  The organization of information is not clear and depends on the particular professional network:  For this reason, it is essential to build a common vocabulary, which includes the most relevant parameters, in order to make professional recommendations  Each offer is composed of a set of parameters, such as location, category, minimum degree, years of experience… 8
  9. 9. INFORMATION RETRIEVAL (III) 9
  10. 10. INFORMATION RETRIEVAL (IV)  Elasticsearch for storing the gathered data  Kibana for the visualization of the Information  Discover and graphically visualize the Information with panels 10
  11. 11. OUTLINE  Motivation  Information Retrieval  Indicators  Recommendation System  Conclusions and Future Work 11
  12. 12.  Selected indicators:  Title  Description  Duration  Category and sub-category  Company name and type  Location: city/province  Requirements: experience, degree title, degree title type, and other requirements  Salary: min-salary, max-salary, currency, and frequency  Source: Infojobs, Monster…  Creation date, old and new offers are stored and maintained in the system 12 INDICATORS (I) Antonio Robles-Gómez, Salvador Ros, Antonio Martínez-Gámez, Roberto Hernández, Agustín C. Caminero, Llanos Tobarra, Rafael Pastor, Jesús Cano. Defining a Novel Ontology for Educational Counselling based on Professional Indicators (http://educate.gast.it.uc3m.es/wp- content/uploads/2016/05/uned-paper.pdf). Workshop on Applied and Practical Learning Analytics (WAPLA2016), in conjunction with EC-TEL 2016 Conference. Lyon, France. September, 2016.
  13. 13.  Example:  Java programmer  This position focuses on programming physical devices  2 years duration  Computers and telecommunications categories / Programing sub-category  Intel company  Location: Madrid, Madrid  Requirements: 3 years experience, Master in Computer Science, B2 English  Salary: 24.000-27.000 euros per year  Infojobs source  Creation date: 2016-06-12 13 INDICATORS (II) Antonio Robles-Gómez, Salvador Ros, Antonio Martínez-Gámez, Roberto Hernández, Agustín C. Caminero, Llanos Tobarra, Rafael Pastor, Jesús Cano. Defining a Novel Ontology for Educational Counselling based on Professional Indicators (http://educate.gast.it.uc3m.es/wp- content/uploads/2016/05/uned-paper.pdf). Workshop on Applied and Practical Learning Analytics (WAPLA2016), in conjunction with EC-TEL 2016 Conference. Lyon, France. September, 2016.
  14. 14. 14 INDICATORS (III) Antonio Robles-Gómez, Salvador Ros, Antonio Martínez-Gámez, Roberto Hernández, Agustín C. Caminero, Llanos Tobarra, Rafael Pastor, Jesús Cano. Defining a Novel Ontology for Educational Counselling based on Professional Indicators (http://educate.gast.it.uc3m.es/wp-content/uploads/2016/05/uned-paper.pdf). Workshop on Applied and Practical Learning Analytics (WAPLA2016), in conjunction with EC-TEL 2016 Conference. Lyon, France. September, 2016.
  15. 15.  As a first approximation:  An educational profile includes the last and previous Degree Titles and Degree Title Types, and the student’ location  Possible recommendations:  A set of offers according to him/her degrees and locations  Additional studies if he/she wants to reach a set of offers with a higher position, more salary, or a more prestigious company  … 15 INDICATORS (IV)
  16. 16. OUTLINE  Motivation  Information Retrieval  Indicators  Recommendation System  Conclusions and Future Work 16
  17. 17. 17 RECOMMENDATION SYSTEM (I)  Developing a web application in order to recommend job offers to University students  Students will additionally be able to perform searches according to certain criteria and offer position features, such as location, type of company, duration…  Visualization panels about job offers (from Kibana) are integrated in the web-application
  18. 18. 18 RECOMMENDATION SYSTEM (II)
  19. 19. 19 RECOMMENDATION SYSTEM (III)
  20. 20. OUTLINE  Motivation  Information Retrieval  Indicators  Recommendation System  Conclusions 20
  21. 21.  Learning does not take place in an isolated context, it is linked with the professional profiles  Several professional sources of information, such as InfoJobs and Monster, have been studied in this work  A set of indicators are presented for professional profiles  A recommendation system has been started to guide students (depending of their type and level of qualification, location…) 21 CONCLUSIONS
  22. 22. USING KIBANA AND ELASTICSEARCH FOR THE RECOMMENDATION OF JOB OFFERS TO STUDENTS Antonio Robles-Gómez, Salvador Ros, Antonio Martínez-Gámez, Roberto Hernández, Llanos Tobarra, Rafael Pastor, Agustín C. Caminero, Jesús Cano {arobles,sros,roberto,llanos,rpastor,accaminero,jcano}@scc.uned.es martinezgamez.antonio@gmail.com

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